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# General Parameters, see comment for each definition
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# General Parameters, see comment for each definition
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# choose the tree booster, 0: tree, 1: linear
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# choose the booster, can be gbtree or gblinear
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booster_type = 0
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booster = gbtree
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# choose logistic regression loss function for binary classification
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# choose logistic regression loss function for binary classification
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objective = binary:logistic
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objective = binary:logistic
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# General Parameters, see comment for each definition
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# General Parameters, see comment for each definition
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# choose the tree booster, 0: tree, 1: linear
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booster_type = 0
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# specify objective
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# specify objective
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objective="rank:pairwise"
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objective="rank:pairwise"
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# General Parameters, see comment for each definition
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# General Parameters, see comment for each definition
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# choose the tree booster, 0: tree, 1: linear
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# choose the tree booster, can also change to gblinear
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booster_type = 0
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booster = gbtree
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# this is the only difference with classification, use reg:linear to do linear classification
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# this is the only difference with classification, use reg:linear to do linear classification
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# when labels are in [0,1] we can also use reg:logistic
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# when labels are in [0,1] we can also use reg:logistic
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objective = reg:linear
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objective = reg:linear
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